EDITOR’S NOTE HYPER-CONVERGED SYSTEMS RISE ABOVE THE HYPE

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SEVEN TIPS FOR PICKING A HYPER-CONVERGED SYSTEM

Hyper-Convergence: Infrastructure in a BoxHyper-converged systems that bundle storage, compute, networking and virtualization come in several types of packages. Examine the types of products available and the features you need to know about when shopping for the best hyper-converged system.

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Traverse the Growing Hyper-Converged Landscape

Today, almost every major vendor offers some form of a hyper-converged infrastructure. By wrapping storage, compute, networking and virtualization together neatly in one box, it may seem like a storage administrator’s dream come true—and, for some, it has been. For use cases such as virtual desktop infrastructure or remote office, an appliance that can be installed in under an hour and scale by simply adding additional nodes is extremely attractive. But that doesn’t mean it’s without its drawbacks.

Because hyper-converged systems package capacity and compute in one appliance, scaling one usually means scaling both. That can result in the purchase of more capacity or compute, whether it’s needed or not. Enterprises that haven’t adopted hyper-convergence may also be concerned about vendor lock-in or the fact that integration with existing infrastructure is difficult.

In an effort to push these products into more enterprises, today’s vendors have been address-ing these problems. Many hyper-converged systems are offered in multiple configurations so administrators can more accurately obtain the resources they need when scaling. Other products are software-only and can be used with commodity hardware, giving administra-tors greater choice when it comes to the ven-dors used in their environments.

As hyper-converged products continue to evolve, and additional vendors enter the mar-ket, IT has more choices than ever. This three-part guide outlines the top considerations for determining whether hyper-convergence is right for your business, and provides an over-view of today’s hyper-converged landscape. n

Sarah WilsonSite Editor, SearchVirtualStorage

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Hyper-Converged Systems Move From Concept to Reality

Hyper-converged infrastructures s are turnkey, self-contained compute systems sold by a single vendor and typically branded by that vendor, or an integrator. These systems are typically composed of multiple physical appliance modules in a scale-out topology referred to as nodes. Each node includes stor-age, a compute engine, networking components and, usually, a hypervisor.

Appliances typically contain between one and four nodes, each of which is an indepen-dent server with CPU and memory that share a common server chassis. Hyper-converged clus-ters usually contain between four and 16 nodes, although some have no specified limit. Hyper-converged infrastructures leverage either vir-tual SAN or clustered file system software to share storage across multiple nodes.

As ready-to-run products, hyper-converged systems include everything needed to create a compute infrastructure including hardware,

software, management functionality and com-prehensive support. Although they don’t have to include a hypervisor, all the leading products available include at least one hypervisor option, and many offer two or more.

HYPER-SCALE CONCEPTS

BECOME HYPER-CONVERGED STORAGE

The basis for the hyper-converged infrastruc-ture product category is a model known as the “Open Storage Platform,” developed initially by large Web companies as a way to store and handle the enormous amounts of data their social media and Internet-based businesses were generating. These hyper-scalers leveraged the economics of industry-standard x86 server hardware and the flexibility of software-defined storage features and functionality, much of which they wrote themselves.

From this origin, hyper-converged vendors

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have productized the open storage platform model, removing the do-it-yourself aspect and consolidating the components into a turnkey appliance.

Product evolution has been largely incremen-tal, with vendors adding model configurations with more storage capacity (flash and hard-disk drives), compute power and memory. While most products use a mix of flash and hard disk storage, there is currently one vendor that offers all-flash configurations exclusively and another that offers only disk drive storage.

The most common approach has been to develop proprietary software that either includes embedded storage and management functions, or runs these functions as virtual machines (VMs) in an embedded hypervisor. This software comes either installed on branded x86 server hardware or on a server from one of the major platform vendors, such as Supermicro, HP or Cisco. Some of these products include: Atlantis HyperScale, HP CS-250 StoreVirtual, Maxta MaxDeploy, Nuta-nix, Dell XC series (using Nutanix software), Scale Computing and SimpliVity.

The other most common approach today

is to use the VMware EVO:RAIL platform. Launched at VMworld in 2014, EVO:RAIL can be thought of as a reference architecture which integrates the storage and management func-tions into the VMware hypervisor. VMware has partnered with most major vendors to sell

EVO:RAIL using branded x86 server hardware that is packaged with VMware’s Virtual SAN software. Some of these products include: Dell EVO:RAIL, EMC VSPEX Blue, Fujitsu PRIME-FLEX, HDS UCP 1000, NetApp EVO:RAIL and SuperMicro SYS-2028TP. Most EVO:RAIL products have essentially the same configura-tions, although there is some unique function-ality provided by the different vendors.

On a per-node basis, hyper-converged infra-structure configurations vary widely, offering

Most EVO:RAIL products have essentially the same configura-tions, although there is some unique function ality provided by the different vendors.

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up to two dozen CPU cores and a half terabyte of memory. Storage options are similarly var-ied providing up to 60 TB of disk and 38 TB of flash capacity per 2U appliance, based on cur-rent research from the Evaluator Group. These are impressive statistics, but can HCI products really handle serious workloads?

WHY HYPER-CONVERGED?

Hyper-converged infrastructures provide a powerful, feature-rich, virtual server environ-ment that’s easy to set up, often in an hour or less, and simple to operate and scale.

Most of the success these products have seen is in relatively small or isolated environments, like departmental computing or remote loca-tions where technical expertise (or headcount) doesn’t support a traditional IT infrastructure. A sweet spot for these products is business environments that have multiple locations and no dedicated IT staff, such as retail chains or branch offices of a company.

Similar to converged systems, hyper- converged architectures simplify the pro-curement process by providing a single SKU

product. But instead of combining multiple rack-level IT components, these products com-bine (or hyper-converge) storage, networking and compute elements into a single, industry-standard server chassis. The result is a data center offering that scales down to smaller configurations and is much simpler to imple-ment, leading to the success these products have enjoyed.

HOW HYPER-CONVERGED

INFRASTRUCTURE PRODUCTS HOLD UP

So if the hyper-converged sweet spot is for use cases such as remote offices or businesses with small IT departments, are they also effective for larger environments? Many environments in which hyper-convergence has been imple-mented may not be particularly high-perfor-mance, but that doesn’t mean these systems lack the horsepower to run real workloads.

When comparing hyper-converged options, tests should resemble real-world conditions. In this case, those would most likely be virtual server environments or virtual desktop infra-structures (VDI).

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A standard measure of system performance is the number of VMs or virtual desktops they can support. Based on Evaluator Group’s cur-rent IOMark VM and IOMark VDI testing, hyper-converged systems can accommodate significant workloads.

HYPER-CONVERGENCE LIMITATIONS:

SCALE, IMPLEMENTATION

When shipped as an appliance, hyper- converged infrastructures include storage, compute and networking. That means that, unlike most traditional IT systems, all of these resources are required to scale together.

Lately, some vendors have started to address the charge that their products only scale capac-ity and computer in lock-step. Most products today (except EVO:RAIL) are available in mul-tiple appliance configurations aimed at increas-ing flexibility, especially in storage. A few vendors even offer storage-only nodes—appli-ances that have minimal CPU and memory. Another vendor allows its hyper-converged product to be connected to an existing scale-out storage product.

But even though most products offer dif-ferent configuration options when adding modules, hyper-converged infrastructures still sub-optimize resources. This results in higher costs as the infrastructure gets bigger.

Hyper-converged systems are, by definition, self-contained, comprehensive IT infrastruc-tures. This means they don’t integrate into an existing data center SAN or compute environ-ment. And since all subsequent modules must be bought from the same vendor, most hyper-converged storage options lock the customer into a single vendor. The success hyper- convergence has seen in the departmental, remote office and small to medium-sized busi-ness space doesn’t always translate into the traditional data center where quick setup and easy operation are less important than scale, flexibility and cost.

HYPER-CONVERGENCE:

NO REPLACEMENT FOR TRADITIONAL IT

As previously mentioned, hyper-converged infrastructure leverages the open storage platform, but is a “product-ization” of that

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concept. There’s nothing to design or integrate. By reducing the infrastructure stack to a cluster of only a few homogeneous appliances, even a small company can implement a virtual server compute environment by connecting modules together like Lego bricks and then following a simple menu-driven startup process.

Hyper-converged systems could be called a “doable do-it-yourself system,” address-ing an issue most companies face one way or another. This the greatest value offered by hyper-converged systems, and the reason hyper-converged infrastructure has established itself in departmental compute environments, in remote office locations and in small com-panies. For these use cases, it has become a mainstream technology. But in traditional data center environments, it’s a different story.

We don’t see hyper-converged systems replacing existing storage and compute infra-structures in medium-sized and larger compa-nies. These organizations typically have more

IT expertise and are more interested in con-solidating IT systems, not adding new silos to manage. They’re also hesitant to be locked into a single vendor as most hyper-converged infra-structure options require. —Eric Slack

We don’t see hyper-converged systems replacing existing storage and compute infra-structures in medium-sized and larger compa nies.

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Hyper-Convergence Comes in Several Flavors

Hyper-converged systems are among the hottest data center technologies today, first gaining mainstream credibility with the 2014 release of EVO:RAIL, a suite of hyper-converged hardware appliances from VMware and its selected partners. Hyper-convergence has developed rapidly, so we’ll look at how it’s defined and why you should give some serious consideration to products in this market seg-ment as you build out your data center storage infrastructure.

Hyper-converged systems take the inte-gration process introduced with a converged infrastructure one step further. The physi-cal components of storage and compute are combined into a single physical form factor, typically a rack-mounted server, using com-modity DAS. Resiliency that would typically be achieved in storage systems through the use of dual-controller-type architectures is implemented in hyper-converged appliances

by scaling out with multiple nodes—a feature that would already be in place to support server resiliency and failover for the hypervisor.

Hyper-converged offerings typically use commodity hardware (although some still have bespoke components) rather than custom ASIC or field-programmable gate array (FPGA) chips used in dedicated storage systems. As a result, the secret sauce or key differentiators of hyper-converged products are baked into the software, which is where the main benefits are derived.

All of today’s hyper-converged products are based on using a server hypervisor, including VMware vSphere, Microsoft Hyper-V and open source KVM.

DISTRIBUTED STORAGE

A key feature of hyper-converged products is the use of distributed storage. DAS compo-nents from each physical server are combined

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to create a logical pool of disk capacity that uses all resources in the scale-out node cluster. This scale-out technique provides a number of benefits, including:

n Resiliency: Data protection is implemented across multiple nodes, providing for the loss of any single disk or even an entire node.

n Performance: I/O for any single virtual machine (VM) can be distributed across an entire cluster of servers. This allows the aggre-gation of I/O bandwidth from many hard disk drives or solid-state drives to be combined. Where data is locally located with a VM, the latency of hyper-converged storage can be lower than accessing an external SAN- connected array.

The use of scale-out technology means local commodity DAS can be used in place of a more expensive dedicated SAN-based storage system. The storage component belonging to hyper-convergence is implemented either as a VM across the infrastructure or, in the case of VMware, as a kernel module (VMware’s Virtual

SAN technology). There’s wide debate on whether integrating storage into the kernel is a better option than keeping it out. Kernel pro-ponents (e.g., VMware) say this kind of method is more resilient than VM-based implementa-tions, as the storage features aren’t impacted by the activity of other virtual machines.

In contrast, those advocating VM-based storage will point to the benefits of separating storage from the hypervisor “operating sys-tem” in the same way that shared SAN storage removed data from the server. Claimed benefits include the ability to upgrade more flexibly, fault isolation (storage doesn’t take compute down), and performance and security isolation.

SOFTWARE-ONLY VS. HARDWARE

HYPER-CONVERGED PRODUCTS

Before we discuss some of the benefits (and disadvantages) of using hyper-converged sys-tems, we should pause a moment and discuss the delivery model. Hyper-converged products can be delivered either as appliances, provid-ing both the hardware and the software, or as software-only products.

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Products that ship as appliances have a num-ber of distinct advantages over pure software offerings:

n Integration tested. Vendors have performed all the integration testing with individual components to ensure the configuration runs efficiently. This means, for example, that the most appropriate host bus adapter and SCSI controllers will be implemented and validated for performance and reliability. As systems are upgraded, vendors have a smaller subset of hardware to test, making the upgrade process easier to control.

n Performance benchmarked. Vendors can benchmark their own products, providing good guidelines as to how many VMs a configuration can be expected to support. This gives users more control over the specific model(s) and quantities they need to purchase to meet a defined requirement.

Software-only proponents say their products remove the “hardware” tax that vendors charge for performing all the component validations. For organizations that are already comfortable

with a specific hardware supplier, a software-only product lets them simply deploy on hard-ware that may already be in place or that can be acquired more cheaply under an existing sup-plier agreement. The downside is the loss of that “one throat to choke,” so diagnosing spe-cific problems (as has been seen with VMware VSAN and SCSI controllers) can be a significant problem.

BENEFITS AND DISADVANTAGES

Whether in appliance or software-only form factors, hyper-converged infrastructure prod-ucts offer users appealing benefits but, as expected, they also have some disadvantages.

n Ease of deployment. This is probably the most widely quoted cost and resource saving of hyper-convergence. Hyper-converged sys-tems can typically be installed and powered up within a matter of hours, rather than the days and weeks needed to implement a large-scale virtual server appliance from scratch. This sav-ing is typically more likely to be experienced by smaller organizations that can’t afford dedicated engineering teams to put bundles

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together. Deployment benefits, of course, were among the strongest attractions of the first converged infrastructure products to appear.

n Lower cost. It’s debatable whether hyper-converged systems are cheaper than deploying a custom virtual server, at least from a hard-ware perspective. However, when operational costs are also taken into consideration, hyper-convergence typically results in lower costs for many organizations.

n Ease of management. Hyper-converged sys-tems can offer users easier management than traditional storage. For example, over time hyper-converged nodes can be retired from a cluster as new ones are added, providing a con-tinuous upgrade path. In addition, vendors are working to improve the ecosystems of their products by adding or improving monitoring and alerting functions that allow them to pro-vide proactive support on hardware failures.

n Resource depletion. Because hyper-converged nodes provide both compute and storage, which itself can be divided into capacity and

performance, there’s always a risk that addi-tional capacity for compute or storage will need to be purchased before the other is fully uti-lized. Vendors have attempted to address this issue by delivering multiple node configura-tions and supporting asymmetric node config-urations, allowing many different node types to be mixed within the same configuration.

BEST USE CASES FOR

HYPER-CONVERGED SYSTEMS

As with any storage technology purchase deci-sion, an evaluation of hyper-converged prod-ucts must start with how they may fit into one’s data center environment.

Initially, these products were adopted pri-marily by small to medium-sized businesses (SMBs), especially those strapped for resources and looking for simplified operations. From an application perspective, there are no restric-tions to the applications that can be deployed, although those requiring high performance may not be as suitable (some vendors are addressing performance issues). So companies are using hyper-converged systems for all types

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of workloads, making it a challenge to tradi-tional vendors selling individual component architectures.

On the other hand, hyper-converged systems may be appropriate for more discrete tasks, such as supporting virtual desktop infrastruc-ture environments or hosting other types of standalone applications.

SAMPLER OF HYPER-CONVERGENCE PRODUCTS

The market of true hyper-converged players is actually quite small, with a few notable appli-ance vendors and a range of software-only vendors.

n Nutanix (appliance). Nutanix Inc. is prob-ably the best known appliance-based hyper-converged provider. The company was founded in 2009 and shipped its first products in 2011 under the branding of “No SAN” long before the term hyper-converged became part of the IT lexicon.

Nutanix’s main product is the Xtreme Com-puting Platform (XCP), a scale-out, node-based product originally built on VMware’s vSphere

ESXi hypervisor that now supports Hyper-V, KVM and the Nutanix’s KVM-based Acropolis. The Xtreme Computing Platform implements storage functionality with a feature called the Nutanix Distributed Filesystem (NDFS), a Google File System-like distributed storage layer that uses features such as MapReduce to implement data deduplication and other space-efficiency features.

Nutanix also partners with Dell to distribute the Nutanix software on Dell hardware, which Dell calls its XC Series.

n SimpliVity (appliance). SimpliVity Inc. was also founded in 2009, launching its first products in April 2013. The company sells OmniCube, a scale-out, node-based appliance. SimpliVity offloads some of OmniCube’s data optimiza-tion tasks to a dedicated PCI Express card that manages data deduplication and compression in real time. This data reduction is globally fed-erated, allowing OmniCube deployments to be geographically dispersed for data protection, as only new data needs to be replicated between locations (after initial VM images have been seeded).

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n Scale Computing (appliance). Scale Comput-ing has delivers hyper-convergence differently, using the open source KVM hypervisor. This has required the company to build its own clustered block-based storage layer known as Scale Computing Reliable Independent Block Engine (SCRIBE). SCRIBE abstracts the physical storage and implements I/O caching across all available resources.

Scale Computing is looking to capitalize on the use of open source software to remove the need to license VMware, or pay “VMware tax.”

n Nimboxx (appliance). Nimboxx Inc. is another hyper-convergence vendor that built its prod-uct using open source software, KVM in par-ticular. The company claims its systems can be deployed in less than 10 minutes and deliver up to 10 times greater storage performance than its competitors.

n Gridstore (appliance). Gridstore Inc. repo- sitioned its Hyper-V-based scale-out stor-age array as a hyper-converged offering called the Gridstore HyperConverged Appliance. The

product line includes systems with all-flash or hybrid storage. Storage capacity can also be increased by adding dedicated hybrid or capac-ity storage nodes.

n VMware EVO:RAIL (Software, reference

model). The EVO:RAIL platform is a collabora-tion between VMware (providing the software) and a range of hardware partners. EVO:RAIL includes components such as VMware Vir-tual SAN to provide the distributed storage layer and quick-start custom software for rapid deployment. The major negative to using EVO:RAIL is the tie-in to VMware as this is the only supported hypervisor; however, that may not be an issue for many customers.

The hyper-converged software-only category includes options from VMware (using VSAN as a software package only), Atlantis Comput-ing, DataCore Symphony, EMC ScaleIO, Maxta Inc. and StarWind. These products aren’t as fully packaged and feature-rich as the appliance offerings, so they require some degree of customer expertise to handle the required integration. —Chris Evans

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Seven Tips for Picking a Hyper-Converged System

With competing implementation mod-els, IT decision makers need help when evalu-ating hyper-converged system offerings for their environments.

The following seven hyper-converged storage system criteria revolve around the basic issues that are central to the objective assessment of the business value of any technology acquisi-tion: cost, availability and fitness to purpose.

1. Hardware dependencies. Some hyper- converged server/storage offerings take the form of pre-engineered appliances or hardware platforms certified by a given hypervisor ven-dor. These create a hardware lock-in situation. But pursuing a hardware-agnostic approach is the only way you can scale various parts of the node at different times based on need or the availability of new technology. The most fun-damental question to ask is: “What hardware will I need to make the product work?”

Hardware dependencies should also be con-sidered with an eye to scaling. The tighter the hardware specification, the less you may be able to scale various components in a modular way. Improvements in component technolo-gies can occur at varying rates, making it dif-ficult to keep track of innovation. Moreover, being locked in to a select list of hardware may increase the cost of the hyper-converged sys-tem offering over time.

2. Multi-nodal architecture. Several hypervisor vendors are selling hyper-converged storage models that begin with a requirement for a minimum of three (or more) clustered nodes. A node usually requires a physical server, a software license, clustering software (whether part of a hypervisor software package, operat-ing system or specialized third-party software), flash storage devices and a storage array or JBOD. The cost per node for one hypervisor

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vendor’s hyper-converged system could range between $8,000 and $11,000 in software licenses and $8,000 and $14,000 in server and storage hardware, according to a recent lab report. Those numbers need to be multiplied by the number of nodes required to create the hyper-converged infrastructure, which is a minimum of three nodes but recommended to be four nodes for availability and performance reasons. By contrast, some third-party hyper-converged server-storage models may require only two physical nodes to start, and can lever-age less-expensive hardware (SATA, rather than SAS disk, for example).

3. Manageability. Leading server hypervisor vendors (and most hyper-converged appliance vendors) prefer their uniform software stack be used to manage all attached resources and the specialized services they offer. When it comes to storage, hypervisor vendors provide inter-faces to administer functions ranging from data mirroring and replication across storage nodes, thin provisioning of storage resources, dedupli-cation and compression, and other tasks once performed on array controllers. In essence, they

centralize the value-added services once touted as differentiators by storage array vendors into a centralized software-based controller. You need to remember that these services need to be examined to ensure the functionality is what you want to use with your infrastructure. For example, just because the compression service is impressive on a given product doesn’t mean its wide-area replication service is the best around.

Be aware that while hyper-converged system vendors agree that storage software services need to be implemented in an off-array soft-ware stack, many eschew the idea of abstract-ing capacity management from the storage array controller. This is a noteworthy limita-tion of many hyper-converged offerings, since it means capacity management is a separate activity that must be performed on each stor-age device, often requiring specialized tools and skills.

4. Hardware utilization efficiencies. Selecting a hyper-converged infrastructure model that optimizes the use of hardware is also important. For example, while most

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hyper-converged products in the market can leverage dynamic RAM and flash memory stor-age technology to create caches and buffers that improve application performance, not all of these products actually use either one effi-ciently or enable the use of the diverse and growing selection of products in the market today. DRAM is better suited for write cach-ing than flash memory, but you might not understand that when reading the marketing literature from certain hyper-converged infra-structure vendors. Often times, flash is recom-mended for purposes to which it is ill-suited, or the hyper-converged vendor limits the cus-tomer’s use of less-expensive componentry or market-leading technologies to accelerate application performance in favor of products that have undergone certification with the vendor.

5. Support for DRAM. Support for DRAM (and perhaps flash memory) acceleration is required for servers running multiple virtual machines. Memory-based caching and buffering make application performance acceleration possible even when the root causes of slow application

performance aren’t storage I/O-related. Ideally, flash technology will also be supported, but it’s not mandatory.

6. Overall “fitness to purpose.” Fitness to pur-pose simply refers to how the technology fits into the environment or setting it is going into. This includes everything from its noise level, electric/HVAC requirements, availability of on-site skilled workers and its compatibility with various hypervisors. It is worthwhile to make a list of facility, workload and user con-straints when considering alternative products.

7. Availability. The clustering/data mirroring functionality of the hyper-converged software you are considering must include mirroring of data in memory caches and buffers, as well as the data stored on solid-state or magnetic disk.

High-availability mirrors must be capable of being tested and verified without disrupting application operation. You should also consider whether mirror failover is best provided as an automatic function or one requiring manual confirmation. Availability is a two-way street: After failover has occurred, capabilities must

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also be provided to fail back. This usually entails buffering mirrored writes until connec-tions can be re-established.

While these seven criteria may not be ex- haustive, they should help to winnow the field of hyper-converged system options to those that will serve your business well in the long haul. For me, personally, I prefer a hyper- converged infrastructure offering that is

hardware- and hypervisor-agnostic, supports DRAM and flash devices from any vendor, and has a disk infrastructure that is direct- and legacy SAN-attached (so I can fully realize my expected ROI from the latter). My final selec-tion would also be one that can virtualize all storage capacity so I can manage capacity allo-cation and special storage services at multiple sites and on heterogeneous storage gear from a single software interface. —Jon William Toigo

HOME

EDITOR’S NOTE

HYPER-CONVERGED

SYSTEMS RISE ABOVE

THE HYPE

HYPER-CONVERGENCE

COMES IN SEVERAL

FLAVORS

SEVEN TIPS

FOR PICKING A

HYPER-CONVERGED

SYSTEM

HYPER-CONVERGENCE: INFRASTRUCTURE IN A BOX18

ABOUT THE

AUTHORS

CHRIS EVANS is an independent consultant with Langton Blue.

ERIC SLACK is a senior analyst with the Evaluator Group.

JON WILLIAM TOIGO is a 30-year IT veteran, CEO and managing principal of Toigo Partners International, and chairman of the Data Management Institute.

Hyper-Convergence: Infrastructure in a Box is a SearchVirtualStorage.com e-publication.

Rich Castagna | VP Editorial/Storage Media Group

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